Open Access

ARTICLE

Collaborative Optimization Strategy for Generator Unit Commitment with Wind-Solar Integrated in the Flexible HVDC Transmission Scene of Offshore Wind Power

Weihan Hao^1,2, Jinchuan Guo², Guanyan Peng², Zeyong Liang², Kepeng Xia³, Peirong Ji^4,*
1 State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China
2 China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou, China
3 XJ Electric Co., Ltd., Xuchang, China
4 School of Electricity and New Energy, Three Gorges University, Yichang, China
* Corresponding Author: Peirong Ji. Email:
(This article belongs to the Special Issue: Advanced Prediction and Control for Offshore Energy: Harvesting, Conversion, Storage and Environmental Sustainability)

Energy Engineering https://doi.org/10.32604/ee.2026.077652

Received 14 December 2025; Accepted 09 February 2026; Published online 20 July 2026

Abstract

The output of offshore wind power (OWP) exhibits significant intermittency and randomness. After being integrated into the power grid via flexible high voltage direct current (HVDC) transmission, it poses higher demands on system operation and scheduling. To overcome the limitations of traditional methods in complex combined scenarios involving OWP flexible HVDC transmission and photovoltaic (PV) grid-connected units, as well as issues such as the tendency of single intelligent optimization algorithms to get trapped in local optima and exhibit sluggish convergence rates, this paper proposes a collaborative optimization strategy for generator unit commitment (UC) with wind-solar integrated in the flexible HVDC transmission scene of OWP, based on an improved hybrid optimization algorithm combining transient-chaotic neural networks (TCNN) and grey wolf (GW) optimization. Firstly, this strategy constructs a collaborative optimization model incorporating OWP, PV, thermal power, and system reserves by quantifying the costs of wind power accommodation and wind curtailment penalties. It incorporates constraints on both positive and negative spinning reserves to tackle uncertainty in OWP and PV output under flexible HVDC transmission paths. Subsequently, an improved hybrid optimization algorithm is proposed by integrating the enhanced GW algorithm with TCNN to solve the UC model involving OWP. Finally, simulation verification is conducted on an improved IEEE 39-bus system for OWP with flexible HVDC output. The results demonstrate that the proposed method can precisely guide the contraction of the solution space through TCNN and achieve efficient global search by combining the improved GW algorithm, providing a fast, economical, and robust solution for UC problems in power systems with a high proportion of OWP transmitted via flexible HVDC.

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Keywords

Offshore wind power; flexible HVDC transmission; unit commitment; chaotic neural network; grey wolf algorithm

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